Are AI and innovation stocks overpriced right now, or is there real value left?

Receipts (6), every quote verbatim from the source

• Damodaran identifies the current AI moment as a hype phase where large price premiums are paid without quantitative justification, and insists that investors must back up those premiums with actual estimates of cash flows, growth, and risk.

  “we are in the hype phase of AI, where it is oversold as the solution to just about every problem known to man, and used to justify large price premiums for the companies in its orbit, without any attempt to quantify and back up these premiums.”

  “if you are paying a high price for an AI effect in a company, it behooves you to put aside your aversion to making estimates, and use your judgment (and data) to arrive at the effect of AI on cashflows, growth and risk, and by extension, on value.”

• Damodaran finds NVIDIA specifically overvalued relative to his estimated intrinsic value, with the stock trading above his computed per-share value even after the DeepSeek-driven markdown.

  “the value per share that I estimate for Nvidia dropped from $87 in September 2024 to $78 in January 2025, much of that change driven by the smaller AI chip market that comes out of the DeepSeek disruption... the stock is overvalued, at its current price of $123 per share, even after the markdown this week.”

  “Since I found Nvidia overvalued in September 2024, when the big AI story was still in place, and Nvidia was trading at $109, $14 lower than todays price, estimating a lower value and comparing to a higher price makes it even more over valued.”

• Damodaran is skeptical that most AI products and services justify massive capital expenditure, viewing most offerings as 'neat' but not transformative enough to command the premiums being priced in.

  “I have remained a skeptic about the product and service side of AI, for much of the last two years... my response to many of these products and services is that, at least for me, they don't do enough for me to bother.”

  “most AI products and services that we will see, as consumers and even as businesses, fall into the 'that's cute' or 'how neat' category, rather than into the 'that would change my life'... it has also struck me as overkill to expend tens of billions of dollars building data centers to develop these products”

• Damodaran warns that even if the AI macro story is correct, that does not automatically translate into investment returns, getting the narrative right is not sufficient; the company-level numbers must also support the price paid.

  “even if you buy into the argument that AI will change the ways that we work and play, it does not necessarily follow that investing in AI-related companies will yield returns. In other words, you can get the macro story right, but you need to also consider how that story plays out across companies to be able to generate returns.”

• For NVIDIA to justify its pricing through fundamentals alone, Damodaran argues the company would need explosive revenue growth across multiple massive markets, a 'daunting combination' that strains credulity.

  “there are plausible paths that lead to the current price being a fair value or under value, but these paths require a daunting combination of extraordinary revenue growth and super-normal margins.”

  “you need another market or two, with potential similar to the AI market, where NVIDIA can wield a dominant market share to justify its pricing.”

• Damodaran acknowledges that past mega-winners like NVIDIA have historically offered their best entry points during sharp drawdowns, suggesting that price-to-value discipline, not narrative enthusiasm, is the correct investment lens.

  “Even the biggest winners in the market have had periods when investors have turned intensely negative on their prospects, making them attractive as investments for value-focused investors.”

Extrapolations, not stated positions

• Their thesis that AI is in a 'hype phase' with premiums unjustified by quantified cash-flow estimates [passage 2] would imply that the broader basket of AI-labelled stocks, not just NVIDIA, is similarly at risk of repricing once investors demand fundamental grounding.
• Their view that DeepSeek represents 'the first of many such reality checks for AI' [passage 4] would imply further downward pressure on AI-infrastructure names whose valuations are predicated on uninterrupted, capital-intensive scaling.
• Their argument that refusing to estimate AI's impact 'will create a vacuum that will be filled by arbitrary AI premiums' [passage 6] would imply that stocks lacking rigorous valuation support are particularly vulnerable to sentiment-driven corrections.

Receipts (5), every quote verbatim from the source

• Wood argues innovation stocks are in deep value territory, not a bubble, contrasting them with broad benchmarks she sees as dangerously overvalued.

  “equity benchmarks are selling at record high prices and near record high valuations, 26x for the S&P 500 and 127x for the Nasdaq on a trailing twelve-month basis.”

  “Much like the early years of ARK's research on and investing in electric vehicles (TSLA) and bitcoin, disruptive innovation seems to be in deep value territory.”

• Wood projects that innovation strategies could deliver 30–40% compound annual returns over five years, with opportunities scaling from roughly $10–12 trillion to $200+ trillion over ten years.

  “our forecasts for these platforms suggest that our strategies today could deliver a 30-40% compound annual rate of return during the next five years. In other words, if our research is correct – and I believe that our research on innovation is the best in the financial world – then our strategies will triple to quintuple in value over the next five years.”

  “the opportunities will scale from $10-12 trillion today, or roughly 10% of the global public equity market cap, to $200+ trillion during the next ten years”

• Wood distinguishes the current innovation era from the 1990s tech bubble, arguing that today's companies are investing aggressively for a legitimate, unprecedented reason rather than chasing unproven dreams.

  “they are backward-looking and do not recognize that companies investing aggressively today are sacrificing short term profitability for an important reason: to capitalize on an innovation age the likes of which the world has never witnessed.”

  “too much capital chased too few opportunities – the technologies were prohibitively expensive and not ready for prime time.”

• Wood forecasts that disruptive innovation could account for more than two-thirds of global equity market capitalization by 2030, up from roughly 20% today, with the Mag 6's share of the innovation market shrinking as disruptive competitors grow faster.

  “disruptive innovation could account for more than two-thirds of the global equity market capitalization in 2030.”

  “innovation assets have appreciated faster still, and at $27 trillion make up more than 20% of the global equity market capitalization.”

• Wood warns that conventional 'tried and true' investment strategies anchored to benchmarks will disappoint over the next five to ten years as the five major innovation platforms, DNA sequencing, robotics, energy storage, AI, and blockchain, scale and converge.

  “we believe "tried and true" investment strategies will disappoint during the next five to ten years as DNA sequencing, robotics, energy storage, artificial intelligence, and blockchain technology scale and converge.”

Extrapolations, not stated positions

• Their thesis that innovation assets have grown from ~10% to ~20% of global equity cap [6] while her 2021 deep-value call was made at 10% would imply the opportunity, though partially realized, still has significant runway toward her 2030 two-thirds target.
• Her argument that the Mag 6 will cede share of innovation cap to faster-growing disruptive competitors [1] would imply that pure-play innovation names, rather than mega-cap incumbents, represent the higher-conviction value opportunity going forward.
• Her explicit contrast with the late-1990s bubble (prohibitively expensive, unproven tech) [8] implies she views current AI infrastructure costs as fundamentally different and commercially ready, underpinning her non-bubble conviction.

Receipts (5), every quote verbatim from the source

• Aschenbrenner argues AI revenue is growing at an extraordinary pace, projecting a $100B annual run rate for companies like Google or Microsoft by ~2026, which would make AI products the biggest revenue driver and growth engine for America's largest corporations, with stock markets to follow.

  “plausibly hitting a $100B annual run rate for companies like Google or Microsoft by ~2026, with powerful but pre-AGI systems—that will motivate ever-greater capital mobilization, and total AI investment could be north of $1T annually by 2027.”

  “This would make AI products the biggest revenue driver for America's largest corporations, and by far their biggest area of growth. Forecasts of overall revenue growth for these companies would skyrocket. Stock markets would follow; we might see our first $10T company soon thereafter.”

• Aschenbrenner contends that the economic returns on AI investment have consistently materialized with each 10x scaleup, justifying continued and accelerating capital deployment, suggesting current valuations reflect real, compounding fundamentals rather than speculation alone.

  “So far, every 10x scaleup in AI investment seems to yield the necessary returns. GPT-3.5 unleashed the ChatGPT mania. The estimated $500M cost for the GPT-4 cluster would have been paid off by the reported billions of annual revenue for Microsoft and OpenAI... if the returns on the last GPU order keep materializing, investment will continue to skyrocket (and outpace revenue), plowing in even more capital in a bet that the next 10x will keep paying off.”

• Aschenbrenner frames the AI buildout as comparable to, and likely exceeding, the scale of transformative historical investments like the internet infrastructure boom and the British railway mania, implying the market opportunity is not historically unprecedented and is grounded in real precedent.

  “Between 1996–2001, telecoms invested nearly $1 trillion in today's dollars in building out internet infrastructure. From 1841 to 1850, private British railway investments totaled a cumulative ~40% of British GDP at the time.”

• Aschenbrenner sees the revenue growth trajectory as self-reinforcing: AI revenue is doubling roughly every six months, and very naively extrapolating this trend from a $10B run rate in early 2025 points to a $100B run rate by mid-2026.

  “Reports suggest OpenAI was at a $1B revenue run rate in August 2023, and a $2B revenue run rate in February 2024. That's roughly a doubling every 6 months. If that trend holds, we should see a ~$10B annual run rate by late 2024/early 2025”

  “Very naively extrapolating out the doubling every 6 months, supposing we hit a $10B revenue run rate in early 2025, suggests this would happen mid-2026.”

• Aschenbrenner dismisses the 'just another tech boom' narrative as fundamentally unserious, implying conventional skeptics are missing the empirical reality of AI's compounding progress.

  “It's just another tech boom, the pundits say confidently. But even among those at the SF-epicenter, the discourse has become polarized between two fundamentally unserious rallying cries.”

Extrapolations, not stated positions

• Their thesis that 'every 10x scaleup in AI investment seems to yield the necessary returns' [passage 4] would imply that current high valuations for AI infrastructure and big-tech names are grounded in a demonstrated returns cycle, not pure speculation, though Aschenbrenner does not directly address whether current stock prices are stretched.
• Their projection that 'we might see our first $10T company soon thereafter' [passage 2] would imply that at least some large-cap AI-exposed stocks (plausibly MSFT or GOOGL given their mentioned AI revenue trajectories) have further upside baked into their fundamentals by his framework, but Aschenbrenner makes no explicit price-target or valuation call.
• Their framing of the AI buildout as exceeding the scale of the 1990s internet infrastructure boom [passage 7] would imply that investors comparing today's AI valuations to the dot-com bubble may be underweighting the real industrial and revenue fundamentals Aschenbrenner sees as distinguishing this cycle.

Receipts (5), every quote verbatim from the source

• Nvidia's valuation is currently below its historical average, making it attractively priced relative to its own history, with >50% growth expected on both top and bottom line this year.

  “Nvidia stock trades at a P/E ratio of 40.7 compared to the 3-year median of 55.29. Nvidia is currently trading 26% lower than the median.”

  “The company is expected to see >50% growth on both the top line and the bottom line this year. This growth combined with flat price action for about a year has led to an attractive valuation.”

• Despite Nvidia's attractive near-term valuation, Kindig argues that the more important question for investors is whether capital compounds faster in Nvidia than in AI alternatives, and she believes it does not, having rotated capital to lesser-known AI names with far higher returns.

  “Going back to my introduction, the question for a portfolio manager isn't whether Nvidia is fairly valued today. It's whether the capital compounds faster in Nvidia's stock over the next twelve months than in the many alternatives we've identified.”

  “The same framework that surfaced those opportunities is what tells me Nvidia's 2026 setup may no longer be as rewarding as what I can find elsewhere.”

• Kindig sees real value in specific, lesser-known AI winners that have dramatically outperformed broad tech benchmarks, with the broader AI trade, not just mega-cap names, offering steep upward trajectories.

  “I am still looking for the same thrill of steep upward stock trajectories unique to the AI market; only in different tickers.”

  “The I/O Fund has built a strong track record in lesser-known AI winners, including Bloom Energy, up 1100% since our initial entry last year, an optical networking stock up more than 620% since November, and one of our largest positions at a 10% allocation already up 130% year to date.”

• Broad innovation/tech indices and ETFs are lagging, with the Nasdaq-100 and most influencer-led tech ETFs barely positive or underperforming the broad market, suggesting index-level tech exposure is not where the value lies.

  “The Nasdaq-100 is only up 9% YTD and most influencer-led tech ETFs are lagging the broad market.”

• Kindig maintains a long-term bullish thesis on AI innovation broadly, projecting Nvidia alone reaching $20 trillion by 2030 and identifying AMD as a stock that may outpace even Nvidia's projected 250% return through 2030.

  “While I still believe Nvidia will reach $20 trillion by 2030, I believe much of that 310% return is likely to be back-half weighted in the years of 2028-2030.”

  “Kindig believes that AMD and its opportunity in AI inference may help the stock outpace Nvidia's projected 250% return through 2030.”

Extrapolations, not stated positions

• Their thesis that Nvidia is trading 26% below its 3-year median P/E [passage 7] while delivering >50% top- and bottom-line growth would imply that at least some large-cap AI names are not overpriced on a fundamentals basis, even after years of appreciation.
• Their documented rotation into optical networking, photonics, and power (Bloom Energy) [passages 1, 6] would imply that Kindig sees the best remaining value in AI infrastructure sub-sectors rather than in the most widely-owned mega-cap AI stocks.
• Their view that Nvidia's $20 trillion return is 'back-half weighted in the years of 2028–2030' [passage 3] would imply that near-term upside for the most crowded AI names may be limited, even if the long-run thesis remains intact.